预生产数据库配置
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ETA获取数据.py
30
ETA获取数据.py
@ -706,8 +706,36 @@ edbcodelist = ['CO1 Comdty', 'ovx index', 'C2404194834', 'C2404199738', 'dxy cur
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# 'S5131019','ID00135604','FSGAM1 Index','S5120408','ID00136724', # 新加坡汽柴油
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]
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# eta自有数据指标编码
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modelsindex = {
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'NHITS': 'SELF0000001',
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'Informer':'SELF0000057',
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'LSTM':'SELF0000058',
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'iTransformer':'SELF0000059',
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'TSMixer':'SELF0000060',
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'TSMixerx':'SELF0000061',
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'PatchTST':'SELF0000062',
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'RNN':'SELF0000063',
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'GRU':'SELF0000064',
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'TCN':'SELF0000065',
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'BiTCN':'SELF0000066',
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'DilatedRNN':'SELF0000067',
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'MLP':'SELF0000068',
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'DLinear':'SELF0000069',
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'NLinear':'SELF0000070',
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'TFT':'SELF0000071',
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'FEDformer':'SELF0000072',
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'StemGNN':'SELF0000073',
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'MLPMultivariate':'SELF0000074',
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'TiDE':'SELF0000075',
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'DeepNPTS':'SELF0000076'
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}
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edbcodelist = edbcodelist+list(modelsindex.values())
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### 文件
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data_set = '原油指标数据.xlsx' # 数据集文件
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data_set = '历史预测结果.xlsx' # 数据集文件
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# data_set = 'INE_OIL(1).csv'
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### 文件夹
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dataset = 'yuanyoudataset' # 数据集文件夹
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151
config_jingbo.py
151
config_jingbo.py
@ -90,54 +90,86 @@ data = {
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ClassifyId = 1214
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### 报告上传配置
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# 变量定义--线上环境
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# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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################################################################################################################ 变量定义--线上环境
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login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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# login_data = {
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# "data": {
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# "account": "api_dev",
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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# "terminal": "API"
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# },
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# "funcModule": "API",
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# "funcOperation": "获取token"
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# }
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login_data = {
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"data": {
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"account": "api_dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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# upload_data = {
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# "funcModule":'研究报告信息',
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# "funcOperation":'上传原油价格预测报告',
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# "data":{
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# "ownerAccount":'27663', #报告所属用户账号 27663 - 刘小朋
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# "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '', #文件名称
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# "fileBase64": '' ,#文件内容base64
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# "categoryNo":'yyjgycbg', # 研究报告分类编码
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# "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
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# "reportEmployeeCode":"E40482" ,# 报告人 E40482 - 管理员 0000027663 - 刘小朋
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# "reportDeptCode" :"002000621000", # 报告部门 - 002000621000 SH期货研究部
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# "productGroupCode":"RAW_MATERIAL" # 商品分类
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# }
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# }
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upload_data = {
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"funcModule":'研究报告信息',
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"funcOperation":'上传原油价格预测报告',
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"data":{
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"ownerAccount":'27663', #报告所属用户账号 27663 - 刘小朋
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"reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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"fileName": '', #文件名称
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"fileBase64": '' ,#文件内容base64
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"categoryNo":'yyjgycbg', # 研究报告分类编码
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"smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
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"reportEmployeeCode":"E40482" ,# 报告人 E40482 - 管理员 0000027663 - 刘小朋
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"reportDeptCode" :"002000621000", # 报告部门 - 002000621000 SH期货研究部
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"productGroupCode":"RAW_MATERIAL" # 商品分类
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}
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}
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# warning_data = {
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# "funcModule":'原油特征停更预警',
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# "funcOperation":'原油特征停更预警',
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# "data":{
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# 'WARNING_TYPE_NAME':'特征数据停更预警',
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# 'WARNING_CONTENT':'',
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# 'WARNING_DATE':''
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# }
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# }
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warning_data = {
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"funcModule":'原油特征停更预警',
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"funcOperation":'原油特征停更预警',
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"data":{
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'WARNING_TYPE_NAME':'特征数据停更预警',
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'WARNING_CONTENT':'',
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'WARNING_DATE':''
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}
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}
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# 生产环境数据库
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# url: jdbc:mysql://rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com:3306/jingbo?autoReconnect=true&useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&transformedBitIsBoolean=true&useSSL=false&serverTimezone=GMT%2B8&nullCatalogMeansCurrent=true
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# username: jingbo
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# password: shihua@123
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# table-schema: jingbo
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# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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# port = 3306
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# dbusername ='jingbo'
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# password = 'shihua@123'
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# dbname = 'jingbo'
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# table_name = 'v_tbl_crude_oil_warning'
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# 预生产环境
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# host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
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# port = 3306
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# dbusername ='jingbo'
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# password = 'shihua@123'
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# dbname = 'jingbo-test'
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# table_name = 'v_tbl_crude_oil_warning'
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# # 变量定义--测试环境
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### 线上开关
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# is_train = True # 是否训练
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# is_debug = False # 是否调试
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# is_eta = True # 是否使用eta接口
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# is_timefurture = True # 是否使用时间特征
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# is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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# is_edbcode = False # 特征使用edbcoding列表中的
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# is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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# is_update_eta = True # 预测结果上传到eta
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# is_update_report = True # 是否上传报告
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# is_update_warning_data = True if datetime.datetime.now().weekday() == 1 else False # 是否上传预警数据
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################################################################################################################ 变量定义--测试环境
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login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login"
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upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
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@ -182,17 +214,13 @@ warning_data = {
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}
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}
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### 线上开关
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# is_train = True # 是否训练
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# is_debug = False # 是否调试
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# is_eta = True # 是否使用eta接口
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# is_timefurture = True # 是否使用时间特征
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# is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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# is_edbcode = False # 特征使用edbcoding列表中的
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# is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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# is_update_eta = True # 预测结果上传到eta
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# is_update_report = True # 是否上传报告
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# is_update_warning_data = True if datetime.datetime.now().weekday() == 1 else False # 是否上传预警数据
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# 北京环境数据库
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host = '192.168.101.27'
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port = 3306
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dbusername ='root'
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password = '123456'
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dbname = 'jingbo_test'
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table_name = 'v_tbl_crude_oil_warning'
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### 开关
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@ -204,28 +232,19 @@ is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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is_update_report = False # 是否上传报告
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is_update_warning_data = False # 是否上传预警数据
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is_update_report = True # 是否上传报告
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is_update_warning_data = True # 是否上传预警数据
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### 北京环境数据库jbsh_test
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# url: jdbc:mysql://192.168.101.27:3306/jingbo_test?autoReconnect=true&useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&transformedBitIsBoolean=true&useSSL=false&serverTimezone=GMT%2B8&nullCatalogMeansCurrent=true
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host = '192.168.101.27'
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port = 3306
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dbusername ='root'
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password = '123456'
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dbname = 'jingbo_test'
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table_name = 'v_tbl_crude_oil_warning'
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db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
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# 连接到数据库
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db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
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db_mysql.connect()
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print("数据库连接成功",host,dbname,dbusername)
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# 数据截取日期
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start_year = 2000 # 数据开始年份
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end_time = '2024-11-23' # 数据截取日期
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start_year = 2018 # 数据开始年份
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end_time = '' # 数据截取日期
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freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
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delweekenday = True if freq == 'B' else False # 是否删除周末数据
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is_corr = False # 特征是否参与滞后领先提升相关系数
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@ -259,7 +259,7 @@ def upload_warning_info(df_count):
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# 更新预警数据中的日期和内容
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warning_data['data']['WARNING_DATE'] = warning_date
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warning_data['data']['WARNING_CONTENT'] = content + '2'
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warning_data['data']['WARNING_CONTENT'] = content
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# 调用 upload_warning_data 函数上传预警数据
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upload_warning_data(warning_data)
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@ -109,37 +109,38 @@ def predict_main():
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import datetime
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# 判断当前日期是不是周一
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is_weekday = datetime.datetime.now().weekday() == 0
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# if is_weekday:
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# logger.info('今天是周一，更新预测模型')
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# # 计算最近60天预测残差最低的模型名称
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# model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
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# # 删除空值率为40%以上的列
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# if len(model_results) > 10:
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# model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
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# # 删除空行
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# model_results = model_results.dropna()
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# modelnames = model_results.columns.to_list()[2:]
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# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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# model_results[col] = model_results[col].astype(np.float32)
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# # 计算每个预测值与真实值之间的偏差率
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# for model in modelnames:
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# model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
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# # 获取每行对应的最小偏差率值
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# min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# # 获取每行对应的最小偏差率值对应的列名
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# min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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# # 将列名索引转换为列名
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# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
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# # 取出现次数最多的模型名称
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# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
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# # 保存结果到数据库
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# if not sqlitedb.check_table_exists('most_model'):
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# sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
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# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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if is_weekday:
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logger.info('今天是周一，更新预测模型')
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# 计算最近60天预测残差最低的模型名称
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model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
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# 删除空值率为40%以上的列
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if len(model_results) > 10:
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model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
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# 删除空行
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model_results = model_results.dropna()
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modelnames = model_results.columns.to_list()[2:]
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for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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model_results[col] = model_results[col].astype(np.float32)
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# 计算每个预测值与真实值之间的偏差率
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for model in modelnames:
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model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
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# 获取每行对应的最小偏差率值
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min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# 获取每行对应的最小偏差率值对应的列名
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min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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# 将列名索引转换为列名
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min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
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# 取出现次数最多的模型名称
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most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
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# 保存结果到数据库
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if not sqlitedb.check_table_exists('most_model'):
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sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
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sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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try:
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if is_weekday:
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# if is_weekday:
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if True:
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logger.info('今天是周一，发送特征预警')
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# 上传预警信息到数据库
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warning_data_df = df_zhibiaoliebiao.copy()
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@ -147,6 +148,11 @@ def predict_main():
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# 重命名列名
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warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
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from sqlalchemy import create_engine
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import urllib
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global password
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if '@' in password:
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password = urllib.parse.quote_plus(password)
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engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
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warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
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warning_data_df['TENANT_CODE'] = 'T0004'
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@ -1,16 +1,16 @@
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ds,ACCURACY,PREDICT_DATE,CREAT_DATE,HIGH_PRICE_y,LOW_PRICE_y,MIN_PRICE,MAX_PRICE,Ds_Week,Pre_Week
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2024-12-02,0.0,2024-12-02,2024-11-29,72.89,71.52,81.17999,81.17999,48,48
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2024-12-03,0.0,2024-12-03,2024-11-29,73.93,71.68,80.79155,80.79155,48,48
|
||||
2024-12-04,0.0,2024-12-04,2024-11-29,74.28,72.25,80.650894,80.650894,48,48
|
||||
2024-12-05,0.0,2024-12-05,2024-11-29,72.92,71.8,80.298,80.298,48,48
|
||||
2024-12-06,0.0,2024-12-06,2024-11-29,72.19,70.85,75.50838,80.264275,48,48
|
||||
2024-12-03,1.0,2024-12-03,2024-12-02,73.93,71.68,69.80779,75.83719,48,48
|
||||
2024-12-04,1.0,2024-12-04,2024-12-02,74.28,72.25,71.281296,76.24051,48,48
|
||||
2024-12-05,1.0,2024-12-05,2024-12-02,72.92,71.8,70.52623,75.95541,48,48
|
||||
2024-12-06,1.0,2024-12-06,2024-12-02,72.19,70.85,70.71932,75.99768,48,48
|
||||
2024-12-04,0.9432068965517233,2024-12-04,2024-12-03,74.28,72.25,72.36529,75.89061,48,48
|
||||
2024-12-05,0.6439196428571465,2024-12-05,2024-12-03,72.92,71.8,72.19881,75.72962,48,48
|
||||
2024-12-06,0.04017164179103775,2024-12-06,2024-12-03,72.19,70.85,72.13617,75.97762,48,48
|
||||
2024-12-05,0.0,2024-12-05,2024-12-04,72.92,71.8,82.40537,82.40537,48,48
|
||||
2024-12-06,0.0,2024-12-06,2024-12-04,72.19,70.85,82.0727,82.0727,48,48
|
||||
2024-12-06,0.0,2024-12-06,2024-12-05,72.19,70.85,83.367485,83.367485,48,48
|
||||
2024-11-26,1.0,2024-11-26,2024-11-25,73.8,71.63,71.071556,76.0069,47,47
|
||||
2024-11-27,1.0,2024-11-27,2024-11-25,72.85,71.71,71.003624,75.58056,47,47
|
||||
2024-11-28,0.7893243243243208,2024-11-28,2024-11-25,72.96,71.85,72.08385,76.20426,47,47
|
||||
2024-11-29,1.0,2024-11-29,2024-11-25,73.34,71.75,71.32973,75.70395,47,47
|
||||
2024-11-27,0.6068728070175414,2024-11-27,2024-11-26,72.85,71.71,72.158165,76.17365,47,47
|
||||
2024-11-28,0.8021441441441385,2024-11-28,2024-11-26,72.96,71.85,72.06962,76.447,47,47
|
||||
2024-11-29,0.6082389937106918,2024-11-29,2024-11-26,73.34,71.75,72.3729,76.08291,47,47
|
||||
2024-11-28,1.0,2024-11-28,2024-11-27,72.96,71.85,70.70975,75.20803,47,47
|
||||
2024-11-29,1.0,2024-11-29,2024-11-27,73.34,71.75,69.92311,75.423775,47,47
|
||||
2024-11-29,1.0,2024-11-29,2024-11-28,73.34,71.75,70.43276,75.48062,47,47
|
||||
2024-11-25,0.11832806324110515,2024-11-25,2024-11-22,74.83,72.3,74.53063,76.67314,47,47
|
||||
2024-11-26,0.0,2024-11-26,2024-11-22,73.8,71.63,74.44043,76.874565,47,47
|
||||
2024-11-27,0.0,2024-11-27,2024-11-22,72.85,71.71,74.66318,76.73413,47,47
|
||||
2024-11-28,0.0,2024-11-28,2024-11-22,72.96,71.85,74.70841,77.14105,47,47
|
||||
2024-11-29,0.0,2024-11-29,2024-11-22,73.34,71.75,74.70321,77.74617,47,47
|
||||
|
||||
|
@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 130,
|
||||
"execution_count": 136,
|
||||
"id": "9daadf20-caa6-4b25-901c-6cc3ef563f58",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -10,9 +10,9 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(50, 9)\n",
|
||||
"(75, 9)\n",
|
||||
"(20, 4)\n",
|
||||
"(50, 12)\n",
|
||||
"(75, 12)\n",
|
||||
" id PREDICT_DATE CREAT_DATE MIN_PRICE MAX_PRICE HIGH_PRICE_x LOW_PRICE_x \\\n",
|
||||
"0 1 2024-11-26 2024-11-25 71.071556 76.006900 \n",
|
||||
"1 2 2024-11-27 2024-11-25 71.003624 75.580560 \n",
|
||||
@ -27,18 +27,18 @@
|
||||
"3 2024-11-29 6.0 71.75 73.34 \n",
|
||||
"4 2024-12-02 5.0 71.52 72.89 \n",
|
||||
" id PREDICT_DATE CREAT_DATE MIN_PRICE MAX_PRICE HIGH_PRICE_x \\\n",
|
||||
"45 46 2024-12-09 2024-12-06 68.894770 73.909450 \n",
|
||||
"46 47 2024-12-10 2024-12-06 68.845604 73.921120 \n",
|
||||
"47 48 2024-12-11 2024-12-06 69.181210 73.906260 \n",
|
||||
"48 49 2024-12-12 2024-12-06 69.075340 73.862946 \n",
|
||||
"49 50 2024-12-13 2024-12-06 69.076550 74.007740 \n",
|
||||
"70 71 2024-11-25 2024-11-22 74.53063 76.673140 \n",
|
||||
"71 72 2024-11-26 2024-11-22 74.44043 76.874565 \n",
|
||||
"72 73 2024-11-27 2024-11-22 74.66318 76.734130 \n",
|
||||
"73 74 2024-11-28 2024-11-22 74.70841 77.141050 \n",
|
||||
"74 75 2024-11-29 2024-11-22 74.70321 77.746170 \n",
|
||||
"\n",
|
||||
" LOW_PRICE_x RIGHT_ROTE ds 序号 LOW_PRICE_y HIGH_PRICE_y \n",
|
||||
"45 2024-12-09 NaN NaN NaN \n",
|
||||
"46 2024-12-10 NaN NaN NaN \n",
|
||||
"47 2024-12-11 NaN NaN NaN \n",
|
||||
"48 2024-12-12 NaN NaN NaN \n",
|
||||
"49 2024-12-13 NaN NaN NaN \n"
|
||||
"70 2024-11-25 10.0 72.30 74.83 \n",
|
||||
"71 2024-11-26 9.0 71.63 73.80 \n",
|
||||
"72 2024-11-27 8.0 71.71 72.85 \n",
|
||||
"73 2024-11-28 7.0 71.85 72.96 \n",
|
||||
"74 2024-11-29 6.0 71.75 73.34 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -87,7 +87,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 131,
|
||||
"execution_count": 137,
|
||||
"id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -101,51 +101,14 @@
|
||||
"2 2024-11-28 0.789324 72.96 71.85 72.083850 76.204260\n",
|
||||
"3 2024-11-29 1.000000 73.34 71.75 71.329730 75.703950\n",
|
||||
"4 2024-12-02 0.853412 72.89 71.52 71.720825 76.264275\n",
|
||||
"5 2024-11-27 0.606873 72.85 71.71 72.158165 76.173650\n",
|
||||
"6 2024-11-28 0.802144 72.96 71.85 72.069620 76.447000\n",
|
||||
"7 2024-11-29 0.608239 73.34 71.75 72.372900 76.082910\n",
|
||||
"8 2024-12-02 0.457007 72.89 71.52 72.263900 76.198685\n",
|
||||
"9 2024-12-03 0.758302 73.93 71.68 72.223820 76.041725\n",
|
||||
"10 2024-11-28 1.000000 72.96 71.85 70.709750 75.208030\n",
|
||||
"11 2024-11-29 1.000000 73.34 71.75 69.923110 75.423775\n",
|
||||
"12 2024-12-02 1.000000 72.89 71.52 70.810356 75.786860\n",
|
||||
"13 2024-12-03 1.000000 73.93 71.68 70.814430 75.791115\n",
|
||||
"14 2024-12-04 1.000000 74.28 72.25 70.476650 75.623730\n",
|
||||
"15 2024-11-29 1.000000 73.34 71.75 70.432760 75.480620\n",
|
||||
"16 2024-12-02 1.000000 72.89 71.52 69.651825 75.270940\n",
|
||||
"17 2024-12-03 1.000000 73.93 71.68 70.536285 75.826430\n",
|
||||
"18 2024-12-04 1.000000 74.28 72.25 70.568340 75.770290\n",
|
||||
"19 2024-12-05 1.000000 72.92 71.80 70.222640 76.113310\n",
|
||||
"20 2024-12-02 0.000000 72.89 71.52 81.179990 81.179990\n",
|
||||
"21 2024-12-03 0.000000 73.93 71.68 80.791550 80.791550\n",
|
||||
"22 2024-12-04 0.000000 74.28 72.25 80.650894 80.650894\n",
|
||||
"23 2024-12-05 0.000000 72.92 71.80 80.298000 80.298000\n",
|
||||
"24 2024-12-06 0.000000 72.19 70.85 75.508380 80.264275\n",
|
||||
"25 2024-12-03 1.000000 73.93 71.68 69.807790 75.837190\n",
|
||||
"26 2024-12-04 1.000000 74.28 72.25 71.281296 76.240510\n",
|
||||
"27 2024-12-05 1.000000 72.92 71.80 70.526230 75.955410\n",
|
||||
"28 2024-12-06 1.000000 72.19 70.85 70.719320 75.997680\n",
|
||||
"29 2024-12-09 0.000000 NaN NaN 71.642590 75.828636\n",
|
||||
"30 2024-12-04 0.943207 74.28 72.25 72.365290 75.890610\n",
|
||||
"31 2024-12-05 0.643920 72.92 71.80 72.198810 75.729620\n",
|
||||
"32 2024-12-06 0.040172 72.19 70.85 72.136170 75.977620\n",
|
||||
"33 2024-12-09 0.000000 NaN NaN 72.165790 75.880104\n",
|
||||
"34 2024-12-10 0.000000 NaN NaN 72.000620 75.906100\n",
|
||||
"35 2024-12-05 0.000000 72.92 71.80 82.405370 82.405370\n",
|
||||
"36 2024-12-06 0.000000 72.19 70.85 82.072700 82.072700\n",
|
||||
"37 2024-12-09 0.000000 NaN NaN 82.652700 82.652700\n",
|
||||
"38 2024-12-10 0.000000 NaN NaN 81.920910 81.920910\n",
|
||||
"39 2024-12-11 0.000000 NaN NaN 81.091270 81.091270\n",
|
||||
"40 2024-12-06 0.000000 72.19 70.85 83.367485 83.367485\n",
|
||||
"41 2024-12-09 0.000000 NaN NaN 82.869705 82.869705\n",
|
||||
"42 2024-12-10 0.000000 NaN NaN 82.580550 82.580550\n",
|
||||
"43 2024-12-11 0.000000 NaN NaN 82.442154 82.442154\n",
|
||||
"44 2024-12-12 0.000000 NaN NaN 82.276510 82.276510\n",
|
||||
"45 2024-12-09 0.000000 NaN NaN 68.894770 73.909450\n",
|
||||
"46 2024-12-10 0.000000 NaN NaN 68.845604 73.921120\n",
|
||||
"47 2024-12-11 0.000000 NaN NaN 69.181210 73.906260\n",
|
||||
"48 2024-12-12 0.000000 NaN NaN 69.075340 73.862946\n",
|
||||
"49 2024-12-13 0.000000 NaN NaN 69.076550 74.007740\n"
|
||||
".. ... ... ... ... ... ...\n",
|
||||
"70 2024-11-25 0.118328 74.83 72.30 74.530630 76.673140\n",
|
||||
"71 2024-11-26 0.000000 73.80 71.63 74.440430 76.874565\n",
|
||||
"72 2024-11-27 0.000000 72.85 71.71 74.663180 76.734130\n",
|
||||
"73 2024-11-28 0.000000 72.96 71.85 74.708410 77.141050\n",
|
||||
"74 2024-11-29 0.000000 73.34 71.75 74.703210 77.746170\n",
|
||||
"\n",
|
||||
"[75 rows x 6 columns]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -176,7 +139,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 132,
|
||||
"execution_count": 138,
|
||||
"id": "0f942c69",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -189,7 +152,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 133,
|
||||
"execution_count": 139,
|
||||
"id": "a7b05510",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -293,592 +256,86 @@
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>2024-11-27</td>\n",
|
||||
" <td>0.606873</td>\n",
|
||||
" <td>2024-11-27</td>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>70</th>\n",
|
||||
" <td>2024-11-25</td>\n",
|
||||
" <td>0.118328</td>\n",
|
||||
" <td>2024-11-25</td>\n",
|
||||
" <td>2024-11-22</td>\n",
|
||||
" <td>74.83</td>\n",
|
||||
" <td>72.30</td>\n",
|
||||
" <td>74.530630</td>\n",
|
||||
" <td>76.673140</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>71</th>\n",
|
||||
" <td>2024-11-26</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-11-26</td>\n",
|
||||
" <td>2024-11-22</td>\n",
|
||||
" <td>73.80</td>\n",
|
||||
" <td>71.63</td>\n",
|
||||
" <td>74.440430</td>\n",
|
||||
" <td>76.874565</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>72</th>\n",
|
||||
" <td>2024-11-27</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-11-27</td>\n",
|
||||
" <td>2024-11-22</td>\n",
|
||||
" <td>72.85</td>\n",
|
||||
" <td>71.71</td>\n",
|
||||
" <td>72.158165</td>\n",
|
||||
" <td>76.173650</td>\n",
|
||||
" <td>74.663180</td>\n",
|
||||
" <td>76.734130</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <th>73</th>\n",
|
||||
" <td>2024-11-28</td>\n",
|
||||
" <td>0.802144</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-11-28</td>\n",
|
||||
" <td>2024-11-26</td>\n",
|
||||
" <td>2024-11-22</td>\n",
|
||||
" <td>72.96</td>\n",
|
||||
" <td>71.85</td>\n",
|
||||
" <td>72.069620</td>\n",
|
||||
" <td>76.447000</td>\n",
|
||||
" <td>74.708410</td>\n",
|
||||
" <td>77.141050</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <th>74</th>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>0.608239</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>2024-11-26</td>\n",
|
||||
" <td>2024-11-22</td>\n",
|
||||
" <td>73.34</td>\n",
|
||||
" <td>71.75</td>\n",
|
||||
" <td>72.372900</td>\n",
|
||||
" <td>76.082910</td>\n",
|
||||
" <td>74.703210</td>\n",
|
||||
" <td>77.746170</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>0.457007</td>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>2024-11-26</td>\n",
|
||||
" <td>72.89</td>\n",
|
||||
" <td>71.52</td>\n",
|
||||
" <td>72.263900</td>\n",
|
||||
" <td>76.198685</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>0.758302</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>2024-11-26</td>\n",
|
||||
" <td>73.93</td>\n",
|
||||
" <td>71.68</td>\n",
|
||||
" <td>72.223820</td>\n",
|
||||
" <td>76.041725</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>10</th>\n",
|
||||
" <td>2024-11-28</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-11-28</td>\n",
|
||||
" <td>2024-11-27</td>\n",
|
||||
" <td>72.96</td>\n",
|
||||
" <td>71.85</td>\n",
|
||||
" <td>70.709750</td>\n",
|
||||
" <td>75.208030</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>11</th>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>2024-11-27</td>\n",
|
||||
" <td>73.34</td>\n",
|
||||
" <td>71.75</td>\n",
|
||||
" <td>69.923110</td>\n",
|
||||
" <td>75.423775</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>12</th>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>2024-11-27</td>\n",
|
||||
" <td>72.89</td>\n",
|
||||
" <td>71.52</td>\n",
|
||||
" <td>70.810356</td>\n",
|
||||
" <td>75.786860</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>13</th>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>2024-11-27</td>\n",
|
||||
" <td>73.93</td>\n",
|
||||
" <td>71.68</td>\n",
|
||||
" <td>70.814430</td>\n",
|
||||
" <td>75.791115</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>14</th>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>2024-11-27</td>\n",
|
||||
" <td>74.28</td>\n",
|
||||
" <td>72.25</td>\n",
|
||||
" <td>70.476650</td>\n",
|
||||
" <td>75.623730</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>15</th>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>2024-11-28</td>\n",
|
||||
" <td>73.34</td>\n",
|
||||
" <td>71.75</td>\n",
|
||||
" <td>70.432760</td>\n",
|
||||
" <td>75.480620</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" <td>47</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16</th>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>2024-11-28</td>\n",
|
||||
" <td>72.89</td>\n",
|
||||
" <td>71.52</td>\n",
|
||||
" <td>69.651825</td>\n",
|
||||
" <td>75.270940</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>17</th>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>2024-11-28</td>\n",
|
||||
" <td>73.93</td>\n",
|
||||
" <td>71.68</td>\n",
|
||||
" <td>70.536285</td>\n",
|
||||
" <td>75.826430</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>18</th>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>2024-11-28</td>\n",
|
||||
" <td>74.28</td>\n",
|
||||
" <td>72.25</td>\n",
|
||||
" <td>70.568340</td>\n",
|
||||
" <td>75.770290</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>19</th>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>2024-11-28</td>\n",
|
||||
" <td>72.92</td>\n",
|
||||
" <td>71.80</td>\n",
|
||||
" <td>70.222640</td>\n",
|
||||
" <td>76.113310</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>20</th>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>72.89</td>\n",
|
||||
" <td>71.52</td>\n",
|
||||
" <td>81.179990</td>\n",
|
||||
" <td>81.179990</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>21</th>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>73.93</td>\n",
|
||||
" <td>71.68</td>\n",
|
||||
" <td>80.791550</td>\n",
|
||||
" <td>80.791550</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>22</th>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>74.28</td>\n",
|
||||
" <td>72.25</td>\n",
|
||||
" <td>80.650894</td>\n",
|
||||
" <td>80.650894</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>23</th>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>72.92</td>\n",
|
||||
" <td>71.80</td>\n",
|
||||
" <td>80.298000</td>\n",
|
||||
" <td>80.298000</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>24</th>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>2024-11-29</td>\n",
|
||||
" <td>72.19</td>\n",
|
||||
" <td>70.85</td>\n",
|
||||
" <td>75.508380</td>\n",
|
||||
" <td>80.264275</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>25</th>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>73.93</td>\n",
|
||||
" <td>71.68</td>\n",
|
||||
" <td>69.807790</td>\n",
|
||||
" <td>75.837190</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>26</th>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>74.28</td>\n",
|
||||
" <td>72.25</td>\n",
|
||||
" <td>71.281296</td>\n",
|
||||
" <td>76.240510</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>27</th>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>72.92</td>\n",
|
||||
" <td>71.80</td>\n",
|
||||
" <td>70.526230</td>\n",
|
||||
" <td>75.955410</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>28</th>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>72.19</td>\n",
|
||||
" <td>70.85</td>\n",
|
||||
" <td>70.719320</td>\n",
|
||||
" <td>75.997680</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>29</th>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>2024-12-02</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>71.642590</td>\n",
|
||||
" <td>75.828636</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>30</th>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>0.943207</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>74.28</td>\n",
|
||||
" <td>72.25</td>\n",
|
||||
" <td>72.365290</td>\n",
|
||||
" <td>75.890610</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>31</th>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>0.643920</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>72.92</td>\n",
|
||||
" <td>71.80</td>\n",
|
||||
" <td>72.198810</td>\n",
|
||||
" <td>75.729620</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>32</th>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>0.040172</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>72.19</td>\n",
|
||||
" <td>70.85</td>\n",
|
||||
" <td>72.136170</td>\n",
|
||||
" <td>75.977620</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>33</th>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>72.165790</td>\n",
|
||||
" <td>75.880104</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>34</th>\n",
|
||||
" <td>2024-12-10</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-10</td>\n",
|
||||
" <td>2024-12-03</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>72.000620</td>\n",
|
||||
" <td>75.906100</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>35</th>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>72.92</td>\n",
|
||||
" <td>71.80</td>\n",
|
||||
" <td>82.405370</td>\n",
|
||||
" <td>82.405370</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>36</th>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>72.19</td>\n",
|
||||
" <td>70.85</td>\n",
|
||||
" <td>82.072700</td>\n",
|
||||
" <td>82.072700</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>37</th>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>82.652700</td>\n",
|
||||
" <td>82.652700</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>38</th>\n",
|
||||
" <td>2024-12-10</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-10</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>81.920910</td>\n",
|
||||
" <td>81.920910</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>39</th>\n",
|
||||
" <td>2024-12-11</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-11</td>\n",
|
||||
" <td>2024-12-04</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>81.091270</td>\n",
|
||||
" <td>81.091270</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>40</th>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>72.19</td>\n",
|
||||
" <td>70.85</td>\n",
|
||||
" <td>83.367485</td>\n",
|
||||
" <td>83.367485</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>41</th>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>82.869705</td>\n",
|
||||
" <td>82.869705</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>42</th>\n",
|
||||
" <td>2024-12-10</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-10</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>82.580550</td>\n",
|
||||
" <td>82.580550</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>43</th>\n",
|
||||
" <td>2024-12-11</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-11</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>82.442154</td>\n",
|
||||
" <td>82.442154</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>44</th>\n",
|
||||
" <td>2024-12-12</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-12</td>\n",
|
||||
" <td>2024-12-05</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>82.276510</td>\n",
|
||||
" <td>82.276510</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>45</th>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-09</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>68.894770</td>\n",
|
||||
" <td>73.909450</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>46</th>\n",
|
||||
" <td>2024-12-10</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-10</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>68.845604</td>\n",
|
||||
" <td>73.921120</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>47</th>\n",
|
||||
" <td>2024-12-11</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-11</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>69.181210</td>\n",
|
||||
" <td>73.906260</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>48</th>\n",
|
||||
" <td>2024-12-12</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-12</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>69.075340</td>\n",
|
||||
" <td>73.862946</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>49</th>\n",
|
||||
" <td>2024-12-13</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>2024-12-13</td>\n",
|
||||
" <td>2024-12-06</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>69.076550</td>\n",
|
||||
" <td>74.007740</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" <td>49</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>75 rows × 10 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
@ -888,51 +345,12 @@
|
||||
"2 2024-11-28 0.789324 2024-11-28 2024-11-25 72.96 71.85 \n",
|
||||
"3 2024-11-29 1.000000 2024-11-29 2024-11-25 73.34 71.75 \n",
|
||||
"4 2024-12-02 0.853412 2024-12-02 2024-11-25 72.89 71.52 \n",
|
||||
"5 2024-11-27 0.606873 2024-11-27 2024-11-26 72.85 71.71 \n",
|
||||
"6 2024-11-28 0.802144 2024-11-28 2024-11-26 72.96 71.85 \n",
|
||||
"7 2024-11-29 0.608239 2024-11-29 2024-11-26 73.34 71.75 \n",
|
||||
"8 2024-12-02 0.457007 2024-12-02 2024-11-26 72.89 71.52 \n",
|
||||
"9 2024-12-03 0.758302 2024-12-03 2024-11-26 73.93 71.68 \n",
|
||||
"10 2024-11-28 1.000000 2024-11-28 2024-11-27 72.96 71.85 \n",
|
||||
"11 2024-11-29 1.000000 2024-11-29 2024-11-27 73.34 71.75 \n",
|
||||
"12 2024-12-02 1.000000 2024-12-02 2024-11-27 72.89 71.52 \n",
|
||||
"13 2024-12-03 1.000000 2024-12-03 2024-11-27 73.93 71.68 \n",
|
||||
"14 2024-12-04 1.000000 2024-12-04 2024-11-27 74.28 72.25 \n",
|
||||
"15 2024-11-29 1.000000 2024-11-29 2024-11-28 73.34 71.75 \n",
|
||||
"16 2024-12-02 1.000000 2024-12-02 2024-11-28 72.89 71.52 \n",
|
||||
"17 2024-12-03 1.000000 2024-12-03 2024-11-28 73.93 71.68 \n",
|
||||
"18 2024-12-04 1.000000 2024-12-04 2024-11-28 74.28 72.25 \n",
|
||||
"19 2024-12-05 1.000000 2024-12-05 2024-11-28 72.92 71.80 \n",
|
||||
"20 2024-12-02 0.000000 2024-12-02 2024-11-29 72.89 71.52 \n",
|
||||
"21 2024-12-03 0.000000 2024-12-03 2024-11-29 73.93 71.68 \n",
|
||||
"22 2024-12-04 0.000000 2024-12-04 2024-11-29 74.28 72.25 \n",
|
||||
"23 2024-12-05 0.000000 2024-12-05 2024-11-29 72.92 71.80 \n",
|
||||
"24 2024-12-06 0.000000 2024-12-06 2024-11-29 72.19 70.85 \n",
|
||||
"25 2024-12-03 1.000000 2024-12-03 2024-12-02 73.93 71.68 \n",
|
||||
"26 2024-12-04 1.000000 2024-12-04 2024-12-02 74.28 72.25 \n",
|
||||
"27 2024-12-05 1.000000 2024-12-05 2024-12-02 72.92 71.80 \n",
|
||||
"28 2024-12-06 1.000000 2024-12-06 2024-12-02 72.19 70.85 \n",
|
||||
"29 2024-12-09 0.000000 2024-12-09 2024-12-02 NaN NaN \n",
|
||||
"30 2024-12-04 0.943207 2024-12-04 2024-12-03 74.28 72.25 \n",
|
||||
"31 2024-12-05 0.643920 2024-12-05 2024-12-03 72.92 71.80 \n",
|
||||
"32 2024-12-06 0.040172 2024-12-06 2024-12-03 72.19 70.85 \n",
|
||||
"33 2024-12-09 0.000000 2024-12-09 2024-12-03 NaN NaN \n",
|
||||
"34 2024-12-10 0.000000 2024-12-10 2024-12-03 NaN NaN \n",
|
||||
"35 2024-12-05 0.000000 2024-12-05 2024-12-04 72.92 71.80 \n",
|
||||
"36 2024-12-06 0.000000 2024-12-06 2024-12-04 72.19 70.85 \n",
|
||||
"37 2024-12-09 0.000000 2024-12-09 2024-12-04 NaN NaN \n",
|
||||
"38 2024-12-10 0.000000 2024-12-10 2024-12-04 NaN NaN \n",
|
||||
"39 2024-12-11 0.000000 2024-12-11 2024-12-04 NaN NaN \n",
|
||||
"40 2024-12-06 0.000000 2024-12-06 2024-12-05 72.19 70.85 \n",
|
||||
"41 2024-12-09 0.000000 2024-12-09 2024-12-05 NaN NaN \n",
|
||||
"42 2024-12-10 0.000000 2024-12-10 2024-12-05 NaN NaN \n",
|
||||
"43 2024-12-11 0.000000 2024-12-11 2024-12-05 NaN NaN \n",
|
||||
"44 2024-12-12 0.000000 2024-12-12 2024-12-05 NaN NaN \n",
|
||||
"45 2024-12-09 0.000000 2024-12-09 2024-12-06 NaN NaN \n",
|
||||
"46 2024-12-10 0.000000 2024-12-10 2024-12-06 NaN NaN \n",
|
||||
"47 2024-12-11 0.000000 2024-12-11 2024-12-06 NaN NaN \n",
|
||||
"48 2024-12-12 0.000000 2024-12-12 2024-12-06 NaN NaN \n",
|
||||
"49 2024-12-13 0.000000 2024-12-13 2024-12-06 NaN NaN \n",
|
||||
".. ... ... ... ... ... ... \n",
|
||||
"70 2024-11-25 0.118328 2024-11-25 2024-11-22 74.83 72.30 \n",
|
||||
"71 2024-11-26 0.000000 2024-11-26 2024-11-22 73.80 71.63 \n",
|
||||
"72 2024-11-27 0.000000 2024-11-27 2024-11-22 72.85 71.71 \n",
|
||||
"73 2024-11-28 0.000000 2024-11-28 2024-11-22 72.96 71.85 \n",
|
||||
"74 2024-11-29 0.000000 2024-11-29 2024-11-22 73.34 71.75 \n",
|
||||
"\n",
|
||||
" MIN_PRICE MAX_PRICE Ds_Week Pre_Week \n",
|
||||
"0 71.071556 76.006900 47 47 \n",
|
||||
@ -940,54 +358,17 @@
|
||||
"2 72.083850 76.204260 47 47 \n",
|
||||
"3 71.329730 75.703950 47 47 \n",
|
||||
"4 71.720825 76.264275 48 48 \n",
|
||||
"5 72.158165 76.173650 47 47 \n",
|
||||
"6 72.069620 76.447000 47 47 \n",
|
||||
"7 72.372900 76.082910 47 47 \n",
|
||||
"8 72.263900 76.198685 48 48 \n",
|
||||
"9 72.223820 76.041725 48 48 \n",
|
||||
"10 70.709750 75.208030 47 47 \n",
|
||||
"11 69.923110 75.423775 47 47 \n",
|
||||
"12 70.810356 75.786860 48 48 \n",
|
||||
"13 70.814430 75.791115 48 48 \n",
|
||||
"14 70.476650 75.623730 48 48 \n",
|
||||
"15 70.432760 75.480620 47 47 \n",
|
||||
"16 69.651825 75.270940 48 48 \n",
|
||||
"17 70.536285 75.826430 48 48 \n",
|
||||
"18 70.568340 75.770290 48 48 \n",
|
||||
"19 70.222640 76.113310 48 48 \n",
|
||||
"20 81.179990 81.179990 48 48 \n",
|
||||
"21 80.791550 80.791550 48 48 \n",
|
||||
"22 80.650894 80.650894 48 48 \n",
|
||||
"23 80.298000 80.298000 48 48 \n",
|
||||
"24 75.508380 80.264275 48 48 \n",
|
||||
"25 69.807790 75.837190 48 48 \n",
|
||||
"26 71.281296 76.240510 48 48 \n",
|
||||
"27 70.526230 75.955410 48 48 \n",
|
||||
"28 70.719320 75.997680 48 48 \n",
|
||||
"29 71.642590 75.828636 49 49 \n",
|
||||
"30 72.365290 75.890610 48 48 \n",
|
||||
"31 72.198810 75.729620 48 48 \n",
|
||||
"32 72.136170 75.977620 48 48 \n",
|
||||
"33 72.165790 75.880104 49 49 \n",
|
||||
"34 72.000620 75.906100 49 49 \n",
|
||||
"35 82.405370 82.405370 48 48 \n",
|
||||
"36 82.072700 82.072700 48 48 \n",
|
||||
"37 82.652700 82.652700 49 49 \n",
|
||||
"38 81.920910 81.920910 49 49 \n",
|
||||
"39 81.091270 81.091270 49 49 \n",
|
||||
"40 83.367485 83.367485 48 48 \n",
|
||||
"41 82.869705 82.869705 49 49 \n",
|
||||
"42 82.580550 82.580550 49 49 \n",
|
||||
"43 82.442154 82.442154 49 49 \n",
|
||||
"44 82.276510 82.276510 49 49 \n",
|
||||
"45 68.894770 73.909450 49 49 \n",
|
||||
"46 68.845604 73.921120 49 49 \n",
|
||||
"47 69.181210 73.906260 49 49 \n",
|
||||
"48 69.075340 73.862946 49 49 \n",
|
||||
"49 69.076550 74.007740 49 49 "
|
||||
".. ... ... ... ... \n",
|
||||
"70 74.530630 76.673140 47 47 \n",
|
||||
"71 74.440430 76.874565 47 47 \n",
|
||||
"72 74.663180 76.734130 47 47 \n",
|
||||
"73 74.708410 77.141050 47 47 \n",
|
||||
"74 74.703210 77.746170 47 47 \n",
|
||||
"\n",
|
||||
"[75 rows x 10 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 133,
|
||||
"execution_count": 139,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -998,7 +379,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 134,
|
||||
"execution_count": 140,
|
||||
"id": "1374e354",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -1006,14 +387,20 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['2024-11-29', '2024-11-30', '2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04', '2024-12-05', '2024-12-06']\n",
|
||||
"['2024-11-22', '2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-11-28', '2024-11-29']\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(15, 10)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 取结束日期上一周的日期\n",
|
||||
"endtime = '2024-12-9'\n",
|
||||
"endtime = '2024-12-3'\n",
|
||||
"endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')\n",
|
||||
"up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)\n",
|
||||
"up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)][4:-2]\n",
|
||||
@ -1030,7 +417,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 135,
|
||||
"execution_count": 141,
|
||||
"id": "8aa47e90",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -1038,22 +425,22 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2024-12-02 00:00:00\n",
|
||||
"2024-11-25 00:00:00\n",
|
||||
"权重： 0.07\n",
|
||||
"准确率： 0.0\n",
|
||||
"2024-12-03 00:00:00\n",
|
||||
"准确率： 1.7749209486165771\n",
|
||||
"2024-11-26 00:00:00\n",
|
||||
"权重： 0.13\n",
|
||||
"准确率： 7.5\n",
|
||||
"2024-12-04 00:00:00\n",
|
||||
"2024-11-27 00:00:00\n",
|
||||
"权重： 0.2\n",
|
||||
"准确率： 9.716034482758616\n",
|
||||
"2024-12-05 00:00:00\n",
|
||||
"准确率： 8.034364035087705\n",
|
||||
"2024-11-28 00:00:00\n",
|
||||
"权重： 0.27\n",
|
||||
"准确率： 6.1646986607143\n",
|
||||
"2024-12-06 00:00:00\n",
|
||||
"准确率： 9.718006756756724\n",
|
||||
"2024-11-29 00:00:00\n",
|
||||
"权重： 0.33\n",
|
||||
"准确率： 3.1205149253731137\n",
|
||||
"26.50124806884603\n"
|
||||
"准确率： 10.824716981132076\n",
|
||||
"37.85200872159308\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -1069,14 +456,6 @@
|
||||
"\n",
|
||||
"print(accuracy_rote)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3e69cacc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user